{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"medRxiv"}],"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T14:16:43Z","timestamp":1768573003488,"version":"3.49.0"},"posted":{"date-parts":[[2025,10,28]]},"group-title":"Health Informatics","reference-count":51,"publisher":"openRxiv","license":[{"start":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T00:00:00Z","timestamp":1761609600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2025,10,28]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>Approaches to randomized clinical trial (RCT) implementation greatly changed during the COVID-19 pandemic as clinical trials transitioned to largely virtual implementation; virtual RCT implementation may continue in the future. Without careful consideration, virtual RCTs may exacerbate the historical under-representation of women, the elderly, and racial and ethnic minorities that has occurred in past clinical trials. The following study presents an approach to virtualizing an RCT for an mHealth intervention.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Materials and Methods<\/jats:title>\n                  <jats:p>Participants for the trial were recruited from Federally Qualified Health Centers in medically underserved areas of the New York City metropolitan area. Recruitment began in January 2020 but was paused in March 2020 due to the COVID-19 pandemic\u2019s onset. The research team developed a virtual protocol for recruitment, onboarding, and study implementation.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Study participants were predominantly from immigrant, low-income, and racial and ethnic minority groups. Our virtualization approach included an easier-to-understand consent form, expanded virtual training materials, and greater one-on-one attention during training. Our approach did not lead to significant changes in the population we recruited into the study or introduce additional biases and restriction.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Discussion<\/jats:title>\n                  <jats:p>Our study represents an early investigation into how the change from in-person clinical trial recruitment and study implementation to virtual during the COVID-19 pandemic may impact recruitment of participants from medically underserved communities into RCTs.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>This approach may be used in future trials testing mHealth and other technological interventions without exacerbating the under-representation of medically underserved populations.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Highlights<\/jats:title>\n                  <jats:list list-type=\"bullet\">\n                    <jats:list-item>\n                      <jats:p>The COVID-19 pandemic accelerated the shift to virtual randomized clinical trials (RCTs), raising concerns about exacerbating the under-representation of marginalized groups.<\/jats:p>\n                    <\/jats:list-item>\n                    <jats:list-item>\n                      <jats:p>This study implemented a fully virtual RCT for a mHealth intervention with consideration of inclusivity.<\/jats:p>\n                    <\/jats:list-item>\n                    <jats:list-item>\n                      <jats:p>Participants were recruited from Federally Qualified Health Centers in medically underserved areas of New York City, with most coming from immigrant, low-income, and minority communities.<\/jats:p>\n                    <\/jats:list-item>\n                    <jats:list-item>\n                      <jats:p>Adaptations such as simplified consent, expanded virtual training, and individualized support ensured accessibility without altering participant demographics or introducing bias, providing a replicable model for equitable virtual trials.<\/jats:p>\n                    <\/jats:list-item>\n                  <\/jats:list>\n                <\/jats:sec>","DOI":"10.1101\/2025.10.27.25338889","type":"posted-content","created":{"date-parts":[[2025,10,28]],"date-time":"2025-10-28T23:05:14Z","timestamp":1761692714000},"source":"Crossref","is-referenced-by-count":0,"title":["Impact of Clinical Trial Virtualization on Recruitment in Underserved Communities for a Type 2 Diabetes mHealth Intervention"],"prefix":"10.64898","author":[{"given":"Elizabeth","family":"Campbell","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrea","family":"Cassells","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"TJ","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jacqeline Cortez","family":"Lainez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arlene","family":"Smaldone","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pooja","family":"Desai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haomiao","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"George","family":"Hripcsak","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonathan","family":"Tobin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lena","family":"Mamykina","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"key":"2025103007150611000_2025.10.27.25338889v1.1","doi-asserted-by":"crossref","first-page":"100370","DOI":"10.1016\/j.cosrev.2021.100370","article-title":"Role of machine learning in medical research: A survey","volume":"40","year":"2021","journal-title":"Computer science review"},{"key":"2025103007150611000_2025.10.27.25338889v1.2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2019.101785"},{"issue":"9","key":"2025103007150611000_2025.10.27.25338889v1.3","doi-asserted-by":"crossref","first-page":"e2233946","DOI":"10.1001\/jamanetworkopen.2022.33946","article-title":"Randomized clinical trials of machine learning interventions in health care: a systematic review","volume":"5","year":"2022","journal-title":"JAMA Network Open"},{"key":"2025103007150611000_2025.10.27.25338889v1.4","doi-asserted-by":"crossref","unstructured":"Siontis GC , Sweda R , Noseworthy PA , Friedman PA , Siontis KC , Patel CJ . 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